There has been an increasing demand for shield tunnel construction due to the extensive utilization and limited land in metropolitan cities. However, the behaviors of soils and rocks exhibit a high level of uncertainty in material modeling. Artificial Intelligence (AI) techniques exhibit huge potential in addressing geotechnical issues that involve non-linear soil-structure interaction. This paper aims to review AI-driven prediction of tunneling-induced surface settlement, focusing on aspects of dataset establishment, input feature selection, and hyperparameter optimization. An overview of AI key applications in surface settlement prediction over the past decades is compiled. Furthermore, the capabilities and limitations of diverse AI techniques are discussed, guiding the selection of methodologies for different scenarios. Subsequently, recent developments such as AI variants, the latest optimization algorithms, and cutting-edge methods are illustrated. Lastly, possible countermeasures of AI for challenges in pragmatic applications are proposed, offering orientations for further research in AI-driven tunneling-induced surface settlement prediction.

中文翻译：

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人工智能驱动的隧道掘进诱发地表沉降预测


由于大城市的广泛利用和有限的土地，对盾构隧道建设的需求不断增加。然而，土壤和岩石的行为在材料建模中表现出高度的不确定性。人工智能 （AI） 技术在解决涉及非线性土壤-结构相互作用的岩土工程问题方面表现出巨大的潜力。本文旨在回顾 AI 驱动的隧道诱导地表沉降预测，重点关注数据集建立、输入特征选择和超参数优化等方面。概述了过去几十年 AI 在地表沉降预测中的关键应用。此外，还讨论了不同 AI 技术的能力和局限性，指导了针对不同场景的方法选择。随后，说明了 AI 变体、最新优化算法和尖端方法等最新发展。最后，提出了人工智能应对实际应用中挑战的可能对策，为人工智能驱动的隧道诱导地表沉降预测的进一步研究提供了方向。